Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations14000
Missing cells7705
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory116.0 B

Variable types

DateTime1
Numeric8
Categorical5
Text2

Alerts

month is highly overall correlated with quarterHigh correlation
quarter is highly overall correlated with monthHigh correlation
residents is highly overall correlated with water_consumptionHigh correlation
water_consumption is highly overall correlated with residentsHigh correlation
apartment_type has 426 (3.0%) missing values Missing
temperature has 441 (3.1%) missing values Missing
income_level has 426 (3.0%) missing values Missing
amenities has 5997 (42.8%) missing values Missing
appliance_usage has 415 (3.0%) missing values Missing
timestamp has unique values Unique
guests has 9658 (69.0%) zeros Zeros

Reproduction

Analysis started2025-03-24 06:38:01.482429
Analysis finished2025-03-24 06:38:04.977127
Duration3.49 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

timestamp
Date

Unique 

Distinct14000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size109.5 KiB
Minimum2002-01-01 00:00:00
Maximum2014-10-11 08:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-24T12:08:05.008880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:05.068517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

residents
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0784286
Minimum-99
Maximum5
Zeros0
Zeros (%)0.0%
Negative280
Negative (%)2.0%
Memory size109.5 KiB
2025-03-24T12:08:05.115974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range104
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.2416653
Coefficient of variation (CV)4.4464676
Kurtosis77.677125
Mean2.0784286
Median Absolute Deviation (MAD)1
Skewness-8.5652378
Sum29098
Variance85.408378
MonotonicityNot monotonic
2025-03-24T12:08:05.154101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 4965
35.5%
2 3074
22.0%
5 2547
18.2%
4 2525
18.0%
1 609
 
4.3%
-99 39
 
0.3%
-11 36
 
0.3%
-55 34
 
0.2%
-22 32
 
0.2%
-33 32
 
0.2%
Other values (4) 107
 
0.8%
ValueCountFrequency (%)
-99 39
 
0.3%
-88 31
 
0.2%
-77 28
 
0.2%
-66 23
 
0.2%
-55 34
 
0.2%
-44 25
 
0.2%
-33 32
 
0.2%
-22 32
 
0.2%
-11 36
 
0.3%
1 609
4.3%
ValueCountFrequency (%)
5 2547
18.2%
4 2525
18.0%
3 4965
35.5%
2 3074
22.0%
1 609
 
4.3%
-11 36
 
0.3%
-22 32
 
0.2%
-33 32
 
0.2%
-44 25
 
0.2%
-55 34
 
0.2%

apartment_type
Categorical

Missing 

Distinct7
Distinct (%)0.1%
Missing426
Missing (%)3.0%
Memory size109.5 KiB
2BHK
3157 
1BHK
3019 
Bungalow
1925 
3BHK
1909 
Cottage
1824 
Other values (2)
1740 

Length

Max length8
Median length4
Mean length5.3083837
Min length4

Characters and Unicode

Total characters72056
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStudio
2nd rowCottage
3rd row1BHK
4th rowCottage
5th row2BHK

Common Values

ValueCountFrequency (%)
2BHK 3157
22.6%
1BHK 3019
21.6%
Bungalow 1925
13.8%
3BHK 1909
13.6%
Cottage 1824
13.0%
Studio 1186
 
8.5%
Detached 554
 
4.0%
(Missing) 426
 
3.0%

Length

2025-03-24T12:08:05.198492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T12:08:05.233341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2bhk 3157
23.3%
1bhk 3019
22.2%
bungalow 1925
14.2%
3bhk 1909
14.1%
cottage 1824
13.4%
studio 1186
 
8.7%
detached 554
 
4.1%

Most occurring characters

ValueCountFrequency (%)
B 10010
13.9%
H 8085
11.2%
K 8085
11.2%
t 5388
 
7.5%
o 4935
 
6.8%
a 4303
 
6.0%
g 3749
 
5.2%
2 3157
 
4.4%
u 3111
 
4.3%
1 3019
 
4.2%
Other values (12) 18214
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 10010
13.9%
H 8085
11.2%
K 8085
11.2%
t 5388
 
7.5%
o 4935
 
6.8%
a 4303
 
6.0%
g 3749
 
5.2%
2 3157
 
4.4%
u 3111
 
4.3%
1 3019
 
4.2%
Other values (12) 18214
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 10010
13.9%
H 8085
11.2%
K 8085
11.2%
t 5388
 
7.5%
o 4935
 
6.8%
a 4303
 
6.0%
g 3749
 
5.2%
2 3157
 
4.4%
u 3111
 
4.3%
1 3019
 
4.2%
Other values (12) 18214
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 10010
13.9%
H 8085
11.2%
K 8085
11.2%
t 5388
 
7.5%
o 4935
 
6.8%
a 4303
 
6.0%
g 3749
 
5.2%
2 3157
 
4.4%
u 3111
 
4.3%
1 3019
 
4.2%
Other values (12) 18214
25.3%

temperature
Real number (ℝ)

Missing 

Distinct2490
Distinct (%)18.4%
Missing441
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean22.566559
Minimum10
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.5 KiB
2025-03-24T12:08:05.286736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11.16
Q116.34
median22.58
Q328.85
95-th percentile33.69
Maximum35
Range25
Interquartile range (IQR)12.51

Descriptive statistics

Standard deviation7.2164468
Coefficient of variation (CV)0.31978498
Kurtosis-1.198012
Mean22.566559
Median Absolute Deviation (MAD)6.26
Skewness-0.023018785
Sum305979.98
Variance52.077105
MonotonicityNot monotonic
2025-03-24T12:08:05.339373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.77 14
 
0.1%
24.98 13
 
0.1%
25.57 13
 
0.1%
22.34 13
 
0.1%
21.6 13
 
0.1%
22.15 13
 
0.1%
30.36 13
 
0.1%
26.86 13
 
0.1%
15.59 13
 
0.1%
27.87 13
 
0.1%
Other values (2480) 13428
95.9%
(Missing) 441
 
3.1%
ValueCountFrequency (%)
10 4
 
< 0.1%
10.01 7
0.1%
10.02 5
< 0.1%
10.03 5
< 0.1%
10.04 4
 
< 0.1%
10.05 11
0.1%
10.06 2
 
< 0.1%
10.07 2
 
< 0.1%
10.08 7
0.1%
10.09 6
< 0.1%
ValueCountFrequency (%)
35 3
< 0.1%
34.99 3
< 0.1%
34.98 7
0.1%
34.97 5
< 0.1%
34.96 3
< 0.1%
34.95 2
 
< 0.1%
34.94 3
< 0.1%
34.93 7
0.1%
34.92 3
< 0.1%
34.91 3
< 0.1%
Distinct4515
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Memory size109.5 KiB
2025-03-24T12:08:05.503757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.905
Min length4

Characters and Unicode

Total characters68670
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1495 ?
Unique (%)10.7%

Sample

1st row46.61
2nd row66.11
3rd row60.86
4th row50.58
5th row52.25
ValueCountFrequency (%)
51.69 13
 
0.1%
49.32 12
 
0.1%
53.07 12
 
0.1%
55.48 11
 
0.1%
49.19 11
 
0.1%
62.88 11
 
0.1%
55.05 11
 
0.1%
55.43 11
 
0.1%
55.07 11
 
0.1%
60.5 11
 
0.1%
Other values (4500) 13886
99.2%
2025-03-24T12:08:05.703815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 13619
19.8%
5 9172
13.4%
4 7815
11.4%
6 6909
10.1%
3 5335
 
7.8%
7 4639
 
6.8%
2 4243
 
6.2%
8 4210
 
6.1%
1 4127
 
6.0%
9 4097
 
6.0%
Other values (84) 4504
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 13619
19.8%
5 9172
13.4%
4 7815
11.4%
6 6909
10.1%
3 5335
 
7.8%
7 4639
 
6.8%
2 4243
 
6.2%
8 4210
 
6.1%
1 4127
 
6.0%
9 4097
 
6.0%
Other values (84) 4504
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 13619
19.8%
5 9172
13.4%
4 7815
11.4%
6 6909
10.1%
3 5335
 
7.8%
7 4639
 
6.8%
2 4243
 
6.2%
8 4210
 
6.1%
1 4127
 
6.0%
9 4097
 
6.0%
Other values (84) 4504
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 13619
19.8%
5 9172
13.4%
4 7815
11.4%
6 6909
10.1%
3 5335
 
7.8%
7 4639
 
6.8%
2 4243
 
6.2%
8 4210
 
6.1%
1 4127
 
6.0%
9 4097
 
6.0%
Other values (84) 4504
 
6.6%

water_price
Real number (ℝ)

Distinct210
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65792357
Minimum-99
Maximum3
Zeros0
Zeros (%)0.0%
Negative272
Negative (%)1.9%
Memory size109.5 KiB
2025-03-24T12:08:05.752680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile1.04
Q11.32
median1.63
Q32.1125
95-th percentile2.82
Maximum3
Range102
Interquartile range (IQR)0.7925

Descriptive statistics

Standard deviation8.7657763
Coefficient of variation (CV)13.323396
Kurtosis79.572013
Mean0.65792357
Median Absolute Deviation (MAD)0.36
Skewness-8.7155616
Sum9210.93
Variance76.838834
MonotonicityNot monotonic
2025-03-24T12:08:05.806227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.58 157
 
1.1%
1.7 153
 
1.1%
1.55 149
 
1.1%
1.62 148
 
1.1%
1.63 148
 
1.1%
1.65 146
 
1.0%
1.6 141
 
1.0%
1.67 140
 
1.0%
1.71 140
 
1.0%
1.72 139
 
1.0%
Other values (200) 12539
89.6%
ValueCountFrequency (%)
-99 26
0.2%
-88 33
0.2%
-77 31
0.2%
-66 33
0.2%
-55 27
0.2%
-44 34
0.2%
-33 28
0.2%
-22 33
0.2%
-11 27
0.2%
1 48
0.3%
ValueCountFrequency (%)
3 20
 
0.1%
2.99 39
0.3%
2.98 51
0.4%
2.97 41
0.3%
2.96 42
0.3%
2.95 43
0.3%
2.94 46
0.3%
2.93 31
0.2%
2.92 35
0.2%
2.91 31
0.2%

period_consumption_index
Real number (ℝ)

Distinct450
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1528899
Minimum-0.13078231
Maximum2.3523113
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size109.5 KiB
2025-03-24T12:08:05.857307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.13078231
5-th percentile0.83
Q10.97
median1.15
Q31.33
95-th percentile1.47
Maximum2.3523113
Range2.4830936
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.22904708
Coefficient of variation (CV)0.19867212
Kurtosis0.9489001
Mean1.1528899
Median Absolute Deviation (MAD)0.18
Skewness-0.063877745
Sum16140.459
Variance0.052462566
MonotonicityNot monotonic
2025-03-24T12:08:05.909121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.47 225
 
1.6%
1.43 224
 
1.6%
1.05 216
 
1.5%
1.46 214
 
1.5%
0.87 213
 
1.5%
1.21 213
 
1.5%
0.92 212
 
1.5%
0.91 212
 
1.5%
1.03 211
 
1.5%
1.27 209
 
1.5%
Other values (440) 11851
84.7%
ValueCountFrequency (%)
-0.1307823083 1
< 0.1%
-0.01401255893 1
< 0.1%
0.0271364282 1
< 0.1%
0.07065422985 1
< 0.1%
0.1249019401 1
< 0.1%
0.1282957768 1
< 0.1%
0.1367372194 1
< 0.1%
0.1446200871 1
< 0.1%
0.1586693434 1
< 0.1%
0.166320506 1
< 0.1%
ValueCountFrequency (%)
2.35231127 1
< 0.1%
2.16208798 1
< 0.1%
2.153225826 1
< 0.1%
2.152694911 1
< 0.1%
2.139341172 1
< 0.1%
2.133695183 1
< 0.1%
2.128998887 1
< 0.1%
2.126050447 1
< 0.1%
2.118159918 1
< 0.1%
2.116545335 1
< 0.1%

income_level
Text

Missing 

Distinct420
Distinct (%)3.1%
Missing426
Missing (%)3.0%
Memory size109.5 KiB
2025-03-24T12:08:06.083113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length6
Mean length6.9846766
Min length3

Characters and Unicode

Total characters94810
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique416 ?
Unique (%)3.1%

Sample

1st rowLow
2nd rowUpper Middle
3rd rowMiddle
4th rowMiddle
5th rowMiddle
ValueCountFrequency (%)
middle 9289
53.0%
upper 3966
22.6%
low 2276
 
13.0%
rich 1593
 
9.1%
7 2
 
< 0.1%
2
 
< 0.1%
b 2
 
< 0.1%
x 2
 
< 0.1%
npc<e 1
 
< 0.1%
qzrsg 1
 
< 0.1%
Other values (406) 406
 
2.3%
2025-03-24T12:08:06.281733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 18596
19.6%
e 13287
14.0%
i 10898
11.5%
M 9310
9.8%
l 9309
9.8%
p 7959
8.4%
r 3990
 
4.2%
U 3987
 
4.2%
3966
 
4.2%
o 2298
 
2.4%
Other values (85) 11210
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 18596
19.6%
e 13287
14.0%
i 10898
11.5%
M 9310
9.8%
l 9309
9.8%
p 7959
8.4%
r 3990
 
4.2%
U 3987
 
4.2%
3966
 
4.2%
o 2298
 
2.4%
Other values (85) 11210
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 18596
19.6%
e 13287
14.0%
i 10898
11.5%
M 9310
9.8%
l 9309
9.8%
p 7959
8.4%
r 3990
 
4.2%
U 3987
 
4.2%
3966
 
4.2%
o 2298
 
2.4%
Other values (85) 11210
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 18596
19.6%
e 13287
14.0%
i 10898
11.5%
M 9310
9.8%
l 9309
9.8%
p 7959
8.4%
r 3990
 
4.2%
U 3987
 
4.2%
3966
 
4.2%
o 2298
 
2.4%
Other values (85) 11210
11.8%

guests
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29292857
Minimum-2
Maximum3
Zeros9658
Zeros (%)69.0%
Negative153
Negative (%)1.1%
Memory size109.5 KiB
2025-03-24T12:08:06.317056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum3
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.48916395
Coefficient of variation (CV)1.6699086
Kurtosis-0.27393064
Mean0.29292857
Median Absolute Deviation (MAD)0
Skewness0.73699744
Sum4101
Variance0.23928137
MonotonicityNot monotonic
2025-03-24T12:08:06.349405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 9658
69.0%
1 4123
29.4%
-1 151
 
1.1%
2 65
 
0.5%
-2 2
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
-2 2
 
< 0.1%
-1 151
 
1.1%
0 9658
69.0%
1 4123
29.4%
2 65
 
0.5%
3 1
 
< 0.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
2 65
 
0.5%
1 4123
29.4%
0 9658
69.0%
-1 151
 
1.1%
-2 2
 
< 0.1%

amenities
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing5997
Missing (%)42.8%
Memory size109.5 KiB
Garden
2627 
Swimming Pool
2086 
Fountain
1648 
Jacuzzi
1642 

Length

Max length13
Median length8
Mean length8.4415844
Min length6

Characters and Unicode

Total characters67558
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSwimming Pool
2nd rowSwimming Pool
3rd rowGarden
4th rowFountain
5th rowSwimming Pool

Common Values

ValueCountFrequency (%)
Garden 2627
18.8%
Swimming Pool 2086
 
14.9%
Fountain 1648
 
11.8%
Jacuzzi 1642
 
11.7%
(Missing) 5997
42.8%

Length

2025-03-24T12:08:06.388209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T12:08:06.417556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
garden 2627
26.0%
swimming 2086
20.7%
pool 2086
20.7%
fountain 1648
16.3%
jacuzzi 1642
16.3%

Most occurring characters

ValueCountFrequency (%)
n 8009
 
11.9%
i 7462
 
11.0%
a 5917
 
8.8%
o 5820
 
8.6%
m 4172
 
6.2%
u 3290
 
4.9%
z 3284
 
4.9%
G 2627
 
3.9%
e 2627
 
3.9%
d 2627
 
3.9%
Other values (11) 21723
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67558
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 8009
 
11.9%
i 7462
 
11.0%
a 5917
 
8.8%
o 5820
 
8.6%
m 4172
 
6.2%
u 3290
 
4.9%
z 3284
 
4.9%
G 2627
 
3.9%
e 2627
 
3.9%
d 2627
 
3.9%
Other values (11) 21723
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67558
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 8009
 
11.9%
i 7462
 
11.0%
a 5917
 
8.8%
o 5820
 
8.6%
m 4172
 
6.2%
u 3290
 
4.9%
z 3284
 
4.9%
G 2627
 
3.9%
e 2627
 
3.9%
d 2627
 
3.9%
Other values (11) 21723
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67558
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 8009
 
11.9%
i 7462
 
11.0%
a 5917
 
8.8%
o 5820
 
8.6%
m 4172
 
6.2%
u 3290
 
4.9%
z 3284
 
4.9%
G 2627
 
3.9%
e 2627
 
3.9%
d 2627
 
3.9%
Other values (11) 21723
32.2%

appliance_usage
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing415
Missing (%)3.0%
Memory size109.5 KiB
0.0
10841 
1.0
2744 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters40755
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10841
77.4%
1.0 2744
 
19.6%
(Missing) 415
 
3.0%

Length

2025-03-24T12:08:06.458934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T12:08:06.484406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10841
79.8%
1.0 2744
 
20.2%

Most occurring characters

ValueCountFrequency (%)
0 24426
59.9%
. 13585
33.3%
1 2744
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40755
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24426
59.9%
. 13585
33.3%
1 2744
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40755
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24426
59.9%
. 13585
33.3%
1 2744
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40755
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24426
59.9%
. 13585
33.3%
1 2744
 
6.7%

water_consumption
Real number (ℝ)

High correlation 

Distinct10635
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.46123
Minimum35.54
Maximum531.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.5 KiB
2025-03-24T12:08:06.520409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35.54
5-th percentile70.6865
Q1109.55
median150.375
Q3206.765
95-th percentile304.6915
Maximum531.49
Range495.95
Interquartile range (IQR)97.215

Descriptive statistics

Standard deviation72.873894
Coefficient of variation (CV)0.44310683
Kurtosis0.78575269
Mean164.46123
Median Absolute Deviation (MAD)46.595
Skewness0.91893074
Sum2302457.2
Variance5310.6044
MonotonicityNot monotonic
2025-03-24T12:08:06.571024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136.19 6
 
< 0.1%
166.87 5
 
< 0.1%
138.97 5
 
< 0.1%
127.11 5
 
< 0.1%
118.26 5
 
< 0.1%
153.51 5
 
< 0.1%
114.51 5
 
< 0.1%
125.61 5
 
< 0.1%
144.82 5
 
< 0.1%
153.6 4
 
< 0.1%
Other values (10625) 13950
99.6%
ValueCountFrequency (%)
35.54 1
< 0.1%
37.8 1
< 0.1%
38.32 1
< 0.1%
40.57 1
< 0.1%
40.97 1
< 0.1%
41.19 1
< 0.1%
41.59 1
< 0.1%
41.73 1
< 0.1%
42.25 1
< 0.1%
42.3 1
< 0.1%
ValueCountFrequency (%)
531.49 1
< 0.1%
523.29 1
< 0.1%
504.3 1
< 0.1%
494.24 1
< 0.1%
491.98 1
< 0.1%
485.7 1
< 0.1%
483.29 1
< 0.1%
476.88 1
< 0.1%
475.21 1
< 0.1%
471.27 1
< 0.1%

quarter
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.5 KiB
3
3588 
2
3549 
1
3519 
4
3344 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 3588
25.6%
2 3549
25.4%
1 3519
25.1%
4 3344
23.9%

Length

2025-03-24T12:08:06.739942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T12:08:06.769957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 3588
25.6%
2 3549
25.4%
1 3519
25.1%
4 3344
23.9%

Most occurring characters

ValueCountFrequency (%)
3 3588
25.6%
2 3549
25.4%
1 3519
25.1%
4 3344
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 3588
25.6%
2 3549
25.4%
1 3519
25.1%
4 3344
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 3588
25.6%
2 3549
25.4%
1 3519
25.1%
4 3344
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 3588
25.6%
2 3549
25.4%
1 3519
25.1%
4 3344
23.9%

is_weekend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size109.5 KiB
0
10002 
1
3998 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10002
71.4%
1 3998
 
28.6%

Length

2025-03-24T12:08:06.809848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T12:08:06.836191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 10002
71.4%
1 3998
 
28.6%

Most occurring characters

ValueCountFrequency (%)
0 10002
71.4%
1 3998
 
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10002
71.4%
1 3998
 
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10002
71.4%
1 3998
 
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10002
71.4%
1 3998
 
28.6%

year
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.8954
Minimum2002
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2025-03-24T12:08:06.862517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2002
Q12005
median2008
Q32011
95-th percentile2014
Maximum2014
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6884231
Coefficient of variation (CV)0.0018369598
Kurtosis-1.2020733
Mean2007.8954
Median Absolute Deviation (MAD)3
Skewness0.010049824
Sum28110536
Variance13.604465
MonotonicityIncreasing
2025-03-24T12:08:06.900252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2004 1098
 
7.8%
2008 1098
 
7.8%
2012 1098
 
7.8%
2002 1095
 
7.8%
2003 1095
 
7.8%
2005 1095
 
7.8%
2006 1095
 
7.8%
2007 1095
 
7.8%
2009 1095
 
7.8%
2010 1095
 
7.8%
Other values (3) 3041
21.7%
ValueCountFrequency (%)
2002 1095
7.8%
2003 1095
7.8%
2004 1098
7.8%
2005 1095
7.8%
2006 1095
7.8%
2007 1095
7.8%
2008 1098
7.8%
2009 1095
7.8%
2010 1095
7.8%
2011 1095
7.8%
ValueCountFrequency (%)
2014 851
6.1%
2013 1095
7.8%
2012 1098
7.8%
2011 1095
7.8%
2010 1095
7.8%
2009 1095
7.8%
2008 1098
7.8%
2007 1095
7.8%
2006 1095
7.8%
2005 1095
7.8%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4428571
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2025-03-24T12:08:06.934601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4225721
Coefficient of variation (CV)0.53121962
Kurtosis-1.1892672
Mean6.4428571
Median Absolute Deviation (MAD)3
Skewness0.014464841
Sum90200
Variance11.714
MonotonicityNot monotonic
2025-03-24T12:08:06.973625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1209
8.6%
3 1209
8.6%
5 1209
8.6%
7 1209
8.6%
8 1209
8.6%
4 1170
8.4%
6 1170
8.4%
9 1170
8.4%
10 1148
8.2%
12 1116
8.0%
Other values (2) 2181
15.6%
ValueCountFrequency (%)
1 1209
8.6%
2 1101
7.9%
3 1209
8.6%
4 1170
8.4%
5 1209
8.6%
6 1170
8.4%
7 1209
8.6%
8 1209
8.6%
9 1170
8.4%
10 1148
8.2%
ValueCountFrequency (%)
12 1116
8.0%
11 1080
7.7%
10 1148
8.2%
9 1170
8.4%
8 1209
8.6%
7 1209
8.6%
6 1170
8.4%
5 1209
8.6%
4 1170
8.4%
3 1209
8.6%

Interactions

2025-03-24T12:08:04.457118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:01.933327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.250939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.571420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.083663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.401178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.714303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.028337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.498258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:01.973929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.290534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.611136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.123629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.440635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.753207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.069274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.536743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.014584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.330145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.843615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.163669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.480260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.793588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.110115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.576256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.053140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.370832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.883589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.203535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.519216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.833197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.151491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.614737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.093197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.411184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.922834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.242321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.559366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.872559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.193047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.654115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.131308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.451602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.963493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.281428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.596773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.912030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.233436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.692578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.170356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.492119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.002302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.321319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.634428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.949607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.274663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.733187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.211015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:02.531978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.044073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.362228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.675785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:03.989786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-24T12:08:04.316412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-24T12:08:07.011680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
amenitiesapartment_typeappliance_usageguestsis_weekendmonthperiod_consumption_indexquarterresidentstemperaturewater_consumptionwater_priceyear
amenities1.0000.1920.0110.0100.0000.0000.0000.0000.0110.0000.2540.0030.000
apartment_type0.1921.0000.0130.0000.0000.0000.0120.0000.0000.0000.3710.0000.011
appliance_usage0.0110.0131.0000.0140.0090.0140.0260.0000.0000.0180.1250.0000.000
guests0.0100.0000.0141.0000.000-0.0040.0100.0000.0100.0090.1860.004-0.007
is_weekend0.0000.0000.0090.0001.0000.0000.0000.0000.0050.0190.0000.0300.000
month0.0000.0000.014-0.0040.0001.0000.0041.000-0.010-0.011-0.002-0.002-0.038
period_consumption_index0.0000.0120.0260.0100.0000.0041.0000.0000.001-0.0270.3710.003-0.003
quarter0.0000.0000.0000.0000.0001.0000.0001.0000.0000.0170.0000.0000.036
residents0.0110.0000.0000.0100.005-0.0100.0010.0001.0000.0000.7250.428-0.002
temperature0.0000.0000.0180.0090.019-0.011-0.0270.0170.0001.0000.1480.0050.008
water_consumption0.2540.3710.1250.1860.000-0.0020.3710.0000.7250.1481.0000.4660.009
water_price0.0030.0000.0000.0040.030-0.0020.0030.0000.4280.0050.4661.0000.003
year0.0000.0110.000-0.0070.000-0.038-0.0030.036-0.0020.0080.0090.0031.000

Missing values

2025-03-24T12:08:04.799468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-24T12:08:04.864994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-24T12:08:04.942874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

timestampresidentsapartment_typetemperaturehumiditywater_priceperiod_consumption_indexincome_levelguestsamenitiesappliance_usagewater_consumptionquarteris_weekendyearmonth
02002-01-01 00:00:001Studio15.3146.611.060.97Low0Swimming Pool0.064.851020021
12002-01-01 08:00:004NaN21.0166.112.980.91Upper Middle1Swimming Pool1.0192.501020021
22002-01-01 16:00:002Cottage12.8660.861.441.43Middle0NaN1.0116.621020021
32002-01-02 00:00:0021BHK20.1650.581.480.91Middle-1Garden0.076.961020021
42002-01-02 08:00:002Cottage16.2352.251.141.11Middle0Fountain0.0104.701020021
52002-01-02 16:00:0042BHK22.2353.861.151.46Middle0NaN1.0218.231020021
62002-01-03 00:00:0032BHK10.8357.512.981.07Upper Middle0Swimming Pool0.0135.801020021
72002-01-03 08:00:003Cottage30.3733.881.351.40yePea0Fountain0.0202.291020021
82002-01-03 16:00:004Bungalow16.5757.942.841.47Upper Middle0Garden0.0188.041020021
92002-01-04 00:00:002NaN22.5957.251.110.99Low1NaN1.088.941020021
timestampresidentsapartment_typetemperaturehumiditywater_priceperiod_consumption_indexincome_levelguestsamenitiesappliance_usagewater_consumptionquarteris_weekendyearmonth
139902014-10-08 08:00:0021BHK22.3762.611.221.290000Middle0NaN0.098.9640201410
139912014-10-08 16:00:0033BHK23.9946.912.631.150000Upper Middle0NaN0.0149.4440201410
139922014-10-09 00:00:004Bungalow15.2248.332.450.890000Upper Middle0NaN0.0117.0340201410
139932014-10-09 08:00:0031BHK24.2753.01.001.500000Middle1NaN1.0213.0740201410
139942014-10-09 16:00:0021BHK26.5247.191.472.082106Low0NaN0.0111.6840201410
139952014-10-10 00:00:0021BHK25.6161.51.700.940000Low0NaN0.078.5940201410
139962014-10-10 08:00:0052BHK13.2752.581.881.030000Upper Middle0Garden1.0185.5040201410
139972014-10-10 16:00:0042BHKNaN46.931.221.100000Middle0NaN1.0180.2840201410
139982014-10-11 00:00:0043BHK11.6264.482.861.120000Upper Middle1Swimming Pool0.0212.1941201410
139992014-10-11 08:00:0042BHK23.7844.881.262.133695c&8%11Jacuzzi0.0303.5941201410